Label assignment [1]}, [2]}, [3]}, [4]}, [5]} is a crucial component in object detection. It matches the ground truth of an object with a specific prediction from the model, and thereby provides the supervision signal for training. Prior to DETR, most object detectors [6]}, [7]}, [8]} adopt the one-to-many matching strategy, which assigns each ground truth to multiple predictions based on local spatial relationships. By contrast, DETR makes one-to-one matching between ground truths and predictions by minimizing a global matching loss. This label assignment approach has been followed by various subsequent variants of the detection transformer [9]}, [10]}, [11]}. Despite the merits of avoiding the duplicates removal process, only a small number of object candidates are supervised by the object labels in each iteration. As a result, the model has to obtain enough supervised signals from a larger amount of data or more training epochs.
To tackle this problem, we propose a label augmentation method to provide a richer supervision signal.
